library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(tidyr)
library(ggplot2)
library(purrr)
library(broom)
library(gganimate)
library(cowplot)
devtools::load_all(".")
## ℹ Loading multiverse
## Loading required package: knitr
knit_as_emar()

Multiverse case study

data("durante")

data.raw.study2 <- durante %>%
  mutate(
    Abortion = abs(7 - Abortion) + 1,
    StemCell = abs(7 - StemCell) + 1,
    Marijuana = abs(7 - Marijuana) + 1,
    RichTax = abs(7 - RichTax) + 1,
    StLiving = abs(7 - StLiving) + 1,
    Profit = abs(7 - Profit) + 1,
    FiscConsComp = FreeMarket + PrivSocialSec + RichTax + StLiving + Profit,
    SocConsComp = Marriage + RestrictAbortion + Abortion + StemCell + Marijuana,
    RelComp = round((Rel1 + Rel2 + Rel3)/3, 2)
  )

Multiverse Analysis

To implement a multiverse analysis, we first need to create the js parameter('masfem') object :

M = multiverse()
df = data.raw.study2 %>%
    mutate(ComputedCycleLength = StartDateofLastPeriod - StartDateofPeriodBeforeLast) %>%
    dplyr::filter(TRUE)
df = data.raw.study2 %>%
    mutate(ComputedCycleLength = StartDateofLastPeriod - StartDateofPeriodBeforeLast) %>%
    dplyr::filter(ComputedCycleLength > 25 & ComputedCycleLength < 35)
df = data.raw.study2 %>%
    mutate(ComputedCycleLength = StartDateofLastPeriod - StartDateofPeriodBeforeLast) %>%
    dplyr::filter(ReportedCycleLength > 25 & ReportedCycleLength < 35)
df = data.raw.study2 %>%
  mutate( ComputedCycleLength = StartDateofLastPeriod - StartDateofPeriodBeforeLast )  %>%
  dplyr::filter( branch(cycle_length,
      "cl_option1" ~ TRUE,
      "cl_option2" ~ ComputedCycleLength > 25 & ComputedCycleLength < 35,
      "cl_option3" ~ ReportedCycleLength > 25 & ReportedCycleLength < 35
  ))
df = df %>%
    dplyr::filter(TRUE)
df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)
df = df %>%
    dplyr::filter(TRUE)
df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)
df = df %>%
    dplyr::filter(TRUE)
df = df %>%
    dplyr::filter(Sure1 > 6 | Sure2 > 6)
df = df %>%
    dplyr::filter( branch(certainty,
        "cer_option1" ~ TRUE,
        "cer_option2" ~ Sure1 > 6 | Sure2 > 6
    ))
df = df %>%
    mutate(NextMenstrualOnset = StartDateofLastPeriod + ComputedCycleLength) %>%
    mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
        15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
        1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df = df %>%
    mutate(NextMenstrualOnset = StartDateofLastPeriod + ReportedCycleLength) %>%
    mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
        15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
        1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df = df %>%
    mutate(NextMenstrualOnset = StartDateNext) %>%
    mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
        15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
        1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df = df %>%
    mutate(NextMenstrualOnset = StartDateofLastPeriod + ComputedCycleLength) %>%
    mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
        15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
        1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df = df %>%
    mutate(NextMenstrualOnset = StartDateofLastPeriod + ReportedCycleLength) %>%
    mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
        15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
        1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df = df %>%
    mutate(NextMenstrualOnset = StartDateNext) %>%
    mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
        15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
        1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df = df %>%
    mutate(NextMenstrualOnset = StartDateofLastPeriod + ComputedCycleLength) %>%
    mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
        15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
        1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df = df %>%
    mutate(NextMenstrualOnset = StartDateNext) %>%
    mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
        15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
        1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df = df %>%
    mutate(NextMenstrualOnset = StartDateofLastPeriod + ComputedCycleLength) %>%
    mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
        15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
        1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df = df %>%
    mutate(NextMenstrualOnset = StartDateNext) %>%
    mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
        15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
        1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df = df %>%
    mutate(NextMenstrualOnset = StartDateofLastPeriod + ReportedCycleLength) %>%
    mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
        15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
        1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df = df %>%
    mutate(NextMenstrualOnset = StartDateNext) %>%
    mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
        15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
        1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df = df %>%
    mutate(NextMenstrualOnset = StartDateofLastPeriod + ReportedCycleLength) %>%
    mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
        15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
        1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df = df %>%
    mutate(NextMenstrualOnset = StartDateNext) %>%
    mutate(CycleDay = 28 - (NextMenstrualOnset - DateTesting), CycleDay = ifelse(WorkerID ==
        15, 11, ifelse(WorkerID == 16, 18, CycleDay)), CycleDay = ifelse(CycleDay >
        1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28)))
df = df %>%
    mutate(NextMenstrualOnset = branch(menstrual_calculation,
        "mc_option1" %when% (cycle_length != "cl_option3") ~ StartDateofLastPeriod + ComputedCycleLength,
        "mc_option2" %when% (cycle_length != "cl_option2") ~ StartDateofLastPeriod + ReportedCycleLength,
        "mc_option3" ~ StartDateNext)
    )  %>%
    mutate(
      CycleDay = 28 - (NextMenstrualOnset - DateTesting),
      CycleDay = ifelse(WorkerID == 15, 11, ifelse(WorkerID == 16, 18, CycleDay)),
      CycleDay = ifelse(CycleDay > 1 & CycleDay < 28, CycleDay, ifelse(CycleDay < 1, 1, 28))
    )
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 27, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
        18 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 27, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
        18 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 27, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
        18 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 27, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
        18 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 27, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
        18 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 27, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
        18 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 27, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
        18 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 27, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
        18 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 27, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
        18 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 27, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
        18 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 27, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
        18 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 27, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
        18 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 27, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
        18 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >=
        17 & CycleDay <= 27, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >=
        18 & CycleDay <= 25, "low", NA))))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low")))
df = df %>%
    mutate(Fertility = factor(ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low")))
df = df %>%
    mutate( Fertility = branch( fertile,
        "fer_option1" ~ factor( ifelse(CycleDay >= 7 & CycleDay <= 14, "high", ifelse(CycleDay >= 17 & CycleDay <= 25, "low", NA)) ),
        "fer_option2" ~ factor( ifelse(CycleDay >= 6 & CycleDay <= 14, "high", ifelse(CycleDay >= 17 & CycleDay <= 27, "low", NA)) ),
        "fer_option3" ~ factor( ifelse(CycleDay >= 9 & CycleDay <= 17, "high", ifelse(CycleDay >= 18 & CycleDay <= 25, "low", NA)) ),
        "fer_option4" ~ factor( ifelse(CycleDay >= 8 & CycleDay <= 14, "high", "low") ),
        "fer_option5" ~ factor( ifelse(CycleDay >= 8 & CycleDay <= 17, "high", "low") )
    ))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1 | Relationship ==
        2, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", "Relationship")))
df = df %>%
    mutate(RelationshipStatus = factor(ifelse(Relationship == 1, "Single", ifelse(Relationship ==
        3 | Relationship == 4, "Relationship", NA))))
df = df %>%
    mutate(RelationshipStatus = branch(relationship_status,
        "rs_option1" ~ factor(ifelse(Relationship==1 | Relationship==2, 'Single', 'Relationship')),
        "rs_option2" ~ factor(ifelse(Relationship==1, 'Single', 'Relationship')),
        "rs_option3" ~ factor(ifelse(Relationship==1, 'Single', ifelse(Relationship==3 | Relationship==4, 'Relationship', NA))) )
    )
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm(RelComp ~ Fertility * RelationshipStatus, data = df)
summary_RelComp <- fit_RelComp %>%
    broom::tidy(conf.int = TRUE)
fit_RelComp <- lm( RelComp ~ Fertility * RelationshipStatus, data = df )

summary_RelComp <- fit_RelComp %>% 
    broom::tidy( conf.int = TRUE )
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expand(M)
## # A tibble: 210 × 10
##    .universe cycle_l…¹ certa…² menst…³ fertile relat…⁴ .parameter…⁵ .code       
##        <int> <chr>     <chr>   <chr>   <chr>   <chr>   <list>       <list>      
##  1         1 cl_optio… cer_op… mc_opt… fer_op… rs_opt… <named list> <named list>
##  2         2 cl_optio… cer_op… mc_opt… fer_op… rs_opt… <named list> <named list>
##  3         3 cl_optio… cer_op… mc_opt… fer_op… rs_opt… <named list> <named list>
##  4         4 cl_optio… cer_op… mc_opt… fer_op… rs_opt… <named list> <named list>
##  5         5 cl_optio… cer_op… mc_opt… fer_op… rs_opt… <named list> <named list>
##  6         6 cl_optio… cer_op… mc_opt… fer_op… rs_opt… <named list> <named list>
##  7         7 cl_optio… cer_op… mc_opt… fer_op… rs_opt… <named list> <named list>
##  8         8 cl_optio… cer_op… mc_opt… fer_op… rs_opt… <named list> <named list>
##  9         9 cl_optio… cer_op… mc_opt… fer_op… rs_opt… <named list> <named list>
## 10        10 cl_optio… cer_op… mc_opt… fer_op… rs_opt… <named list> <named list>
## # … with 200 more rows, 2 more variables: .results <list>, .errors <list>, and
## #   abbreviated variable names ¹​cycle_length, ²​certainty,
## #   ³​menstrual_calculation, ⁴​relationship_status, ⁵​.parameter_assignment
## # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
# don't need this when compiling as package compiles everything
# execute_multiverse(M)
extract_results_json = function (multiverse, summary_obj, filename) {
  if (!is.multiverse(multiverse)) stop(deparse(multiverse), " needs to be an object of class multiverse")
  
  summary_obj = as_name(enquo(summary_obj))
  .summary_obj_default = extract_variable_from_universe(multiverse, 1, summary_obj)
  
  if (!tibble::is_tibble(.summary_obj_default)) stop(summary_obj, " declared inside the multiverse analysis needs to be an object of class tibble or data.frame; please create ", summary_obj, " using broom::tidy or an analogous function")
  
  if (!all(c("term", "estimate", "std.error") %in% names(.summary_obj_default))) stop(summary_obj, " declared inside the multiverse analysis needs to contain the following columns: `term`, `estimate` and `std.error`")
  
  ## ISSUE: if the summary_obj has a distributional object
  
  expand(multiverse) %>%
    extract_variables(!!sym(summary_obj)) %>%
    select(-.code, -.results, -.errors) %>%
    rename(results = summary_obj) %>%
    unnest(results) %>%
    mutate(
      # do we want to perform any text processing on the output of broom::tidy?
      # I think we want the user to do these modifications instead of trying to do this on our own??
      # term = ifelse(term == "(Intercept)", 'Intercept', term)
      min = estimate - 5*std.error,
      max = estimate + 5*std.error
    ) %>%
    group_by(term) %>%
    mutate(min = min(min), max = max(max)) %>%
    mutate(
    cdf.x = pmap(list(min, max, estimate, std.error), ~ seq(..1, ..2, length.out = 101)),
    cdf.y = pmap(list(cdf.x, estimate, std.error), ~ pnorm(..1, ..2, ..3))
    ) %>%
    nest(results = c(term:cdf.y)) %>%
    jsonlite::write_json(filename, pretty = TRUE)
}

extract_results_json(M, fit.summary, 'data2.json')